{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Уровень 0:\n",
    "\n",
    "- Создайте матрицу 3 на 3, заполненную числами от 1 до 9\n",
    "- Прибавьте к ней матрицу с единицами на диагонале\n",
    "- Умножьте полученную матрицу на вектор (1, 2, 3)\n",
    "- Посчитайте обратную матрицу к полученной матрице"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = np.arange(1, 10).reshape(3,3)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.,  2.,  3.],\n",
       "       [ 4.,  6.,  6.],\n",
       "       [ 7.,  8., 10.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = matrix + np.eye(3)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15., 34., 53.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix @ np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-6.00000000e+00, -2.00000000e+00,  3.00000000e+00],\n",
       "       [-1.00000000e+00,  5.00000000e-01, -2.33146835e-16],\n",
       "       [ 5.00000000e+00,  1.00000000e+00, -2.00000000e+00]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.inv(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Уровень 1:\n",
    "Дан объект в 2d пространстве.\n",
    "\n",
    "При помощи матричных операций выполните:\n",
    "\n",
    "- Уменьшить объект в два раза\n",
    "- Повернуть на 130 градусов\n",
    "- Отразить объект относительно прямой y=x\n",
    "- Выполнить смещение объекта и масштабирование (применить 2 матрицы трансформаций). Смещение на (200, 300) и масштабирование на (0.5, 1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = np.array([\n",
    "    [-100, -100, 1],\n",
    "    [-100,  100, 1],\n",
    "    [100,   100, 1],\n",
    "    [0,     0,   1],\n",
    "    [100,  -100, 1],\n",
    "    [-100, -100, 1]\n",
    "])\n",
    "\n",
    "x = A[:,0]\n",
    "y = A[:,1]\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.ylim([-200, 200])\n",
    "plt.xlim([-200, 200])\n",
    "plt.title('Изначальный объект')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A_small = A @ np.array([[0.5, 0,   0],\n",
    "                        [0,   0.5, 0],\n",
    "                        [0,   0,   1]])\n",
    "\n",
    "x = A_small[:,0]\n",
    "y = A_small[:,1]\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.ylim([-200, 200])\n",
    "plt.xlim([-200, 200])\n",
    "plt.title('Уменьшенный в 2 раза объект')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rad_130deg = np.deg2rad(130)\n",
    "\n",
    "\n",
    "A_rotate = A @ np.array([[np.cos(rad_130deg), -np.sin(rad_130deg), 0], \n",
    "                         [np.sin(rad_130deg), np.cos(rad_130deg),  0],\n",
    "                         [0,                  0,                   1]])\n",
    "\n",
    "x = A_rotate[:,0]\n",
    "y = A_rotate[:,1]\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.ylim([-200, 200])\n",
    "plt.xlim([-200, 200])\n",
    "plt.title('Повернутый на 130 градусов объект')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A_y_eq_x = (A @ np.array([[0,   1,   0],\n",
    "                          [1,   0,   0],\n",
    "                          [0,   0,   1]]))\n",
    "x = A_y_eq_x[:,0]\n",
    "y = A_y_eq_x[:,1]\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.ylim([-200, 600])\n",
    "plt.xlim([-200, 600])\n",
    "plt.title('Объект в представлении y=x')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A_scale = (A @ np.array([[1,   0,   0],\n",
    "                         [0,   1,   0],\n",
    "                         [200, 300, 1]])) @ np.array([[0.5, 0,   0],\n",
    "                                                      [0,   1.2, 0],\n",
    "                                                      [0,   0,   1]])\n",
    "x = A_scale[:,0]\n",
    "y = A_scale[:,1]\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.ylim([-200, 600])\n",
    "plt.xlim([-200, 600])\n",
    "plt.title('Объект с перемещением на {200, 300] и масштабированием [0.5, 1.2]')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
